Analyzation and application of PyMC3
DOI:
https://doi.org/10.54097/hset.v9i.1860Keywords:
Machine Learning; Bayesian Method; PyMC3.Abstract
This article studies and analyzes the data of “student study hour”. The purpose of this article is to analyze the relationship between students' learning hours and the score. Data analysis involves machine learning and algorithms. Therefore, these two parts and their related knowledge need to be learned and mastered. In the following part, the article will first introduce the utilization methods, which are machine learning, Bayesian method and PyMC3. Then, the article introduces the details of the analysis steps for the “student study hour” data. The conclusion will be presented at the end of the article, which concludes the linear relation of the data, and the pros and cons of PyMC3.
Downloads
References
NAKRANI, H., 2022. Student Study Hours. [online] Kaggle.com. Available at: <https://www.kaggle.com/datasets/himanshunakrani/student-study-hours> [Accessed 2 September 2022].
Xu, J., 2022. PyMC3 - Introduction and introduction. [online] Blog.csdn.net. Available at: <https://blog.csdn.net/jackxu8/article/details/71080865> [Accessed 2 September 2022].
Zhihu.com. 2022. How to evaluate PyMC3? [online] Available at: <https://www.zhihu.com/question/49044755/answer/203024580> [Accessed 2 September 2022].
Zh.wikipedia.org. 2022. supervised learning-Wikipedia. [online] Available at: <https://zh.wikipedia.org/wiki/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0> [Accessed 2 September 2022].
Artificial intelligence learning library. 2022. Supervised learning. [online] Available at: <https://easyai.tech/ai-definition/supervised-learning/> [Accessed 2 September 2022].
Shui, Y., 2022. [Machine learning note] Unsupervised Learning. [online] Blog.csdn.net. Available at: <https://blog.csdn.net/shuiyixin/article/details/80185207> [Accessed 2 September 2022].
Zhihu. 2022. Machine learning: what is unsupervised learning. [online] Available at: <https://zhuanlan.zhihu.com/p/355600736> [Accessed 2 September 2022].
Nguye, T., 2022. PyMC3 introduction: Python package for probability programming. [online] Blog.csdn.net. Available at: <https://blog.csdn.net/tMb8Z9Vdm66wH68VX1/article/details/112597594> [Accessed 2 September 2022].
Yang, X., 2022. Artificial intelligence and machine translation. 1st ed. Southwest Jiaotong University, p.page 233.
Wang zhong yu, 2010. Small sample non statistical analysis principle of precision instruments. Beijing: Beijing University of Aeronautics and Astronautics Press.
Zhu, X., 2022. Review of Markov chain Monte Carlo method.
Brémaud, P.．Markov chains: Gibbs fields, Monte Carlo simulation, and queues (Vol. 31) ：Springer Science & Business Media，1999：Chapter 2-4, pp.53-156
Zhihu. 2022. Brief introduction to Markov chain. [online] Available at: <https://zhuanlan.zhihu.com/p/448575579> [Accessed 2 September 2022].
Serfozo, R.．Basics of applied stochastic processes. ：Springer Science & Business Media.，2009：pp.1-99, 241-242.
En.wikipedia.org. 2022. Monte Carlo method - Wikipedia. [online] Available at: <https://en.wikipedia.org/wiki/Monte_Carlo_method> [Accessed 2 September 2022].
"What is Machine Learning?". www.ibm.com. Retrieved 2021-08-15.
Li, X., 2022. PyMC3 module. [online] Cnblogs.com. Available at: <https://www.cnblogs.com/lixin2011/p/13975487.html> [Accessed 2 September 2022].
Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892.
Machine learning and pattern recognition "can be viewed as two facets of the same field."
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
